System and method for visualizing workflows in an entire management ecosystem

ABSTRACT

The present invention is directed to a system and method for visualizing workflows in an entire management ecosystem. A computer-implemented method of visualizing live workflows may include: aggregating, by a processor from a database, Key Performance Indicator (KPI) datasets associated with a plurality of teams, each team comprising a plurality of jobs and each job comprising a KPI dataset; obtaining a predefined service Level Agreement (SLA) value associated with each team for measuring each team workflow; and displaying, on a dashboard, the predefined SLA value and the KPI datasets associated with the plurality of teams, wherein each team workflow is visualized as a sequence of bars along a column representing a time axis in real time.

PRIORITY

The present application claims priority to U.S. provisional patentapplication number 62/736,856, filed Sep. 26, 2018, the contents ofwhich are incorporated herein in their entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to workflow management, and morespecifically to systems and methods for visualizing workflows in anentire management ecosystem.

2. Introduction

Retail store management generally involves in multiple workflows or jobsconducted by different teams cooperatively and simultaneously. A team isnormally assigned multiple jobs where each job has defined requirementsor instructions for the team to perform. A team workflow is representedas a combination of performances of sequential jobs. For example, aretail store may handle over 100 workflows which are associated with 600to 700 jobs every day. In some cases, there may be more than 2000 jobsassociated with hundreds of workflows. It is very hard for developers tomonitor and measure such a great amount of workflows and jobs.Additionally, most decision makers may not be sure how their workflowsbehave at a detailed level.

The number of jobs has been constantly increasing in a retailer'se-commence system. Some workflow management tools or software mayautomate task managements in business operations. However, there is aneed to help the decision makers to understand their team workflows at adetailed level and visualize what the exact status of each job is ineach workflow at any time of the day. Moreover, there is a need to havean advanced system to monitor task status by tracking Key PerformanceIndicators (KPIs) with Service Level Agreements (SLA) for workflows inan entire management ecosystem, identify potential problems facing thestore, and develop quick solutions to grow the retailer's business.

SUMMARY

An example computer-implemented method of visualizing live workflowsdisclosed herein can include: aggregating, by a processor from adatabase, Key Performance Indicator (KPI) datasets associated with aplurality of teams, each team comprising a plurality of jobs and eachjob comprising a KPI dataset; obtaining a predefined Service LevelAgreement (SLA) value associated with each team for measuring each teamworkflow; and displaying, on a dashboard, the predefined SLA value andthe KPI datasets associated with the plurality of the teams, whereineach team workflow is visualized as a sequence of bars along a columnrepresenting a time axis in real time.

An example system configured according to the concepts and principlesdisclosed herein can include: at least one processor; a computer programproduct containing executable instructions; and a computer-readablenon-transitory storage medium having the executable instructions storedwhich, when executed by the processor, cause the processor to performoperations comprising: aggregating, by a processor from a database, KeyPerformance Indicator (KPI) datasets associated with a plurality ofteams, each team comprising a plurality of jobs and each job comprisinga KPI dataset; obtaining a predefined Service Level Agreement (SLA)value associated with each team for measuring each team workflow; anddisplaying, on a dashboard, the predefined SLA value and the KPIdatasets associated with the plurality of the teams, wherein each teamworkflow is visualized as a sequence of bars along a column representinga time axis in real time.

A non-transitory computer-readable storage medium having instructionsstored which, when executed by a computing device, cause the computingdevice to perform operations including: aggregating, by a processor froma database, Key Performance Indicator (KPI) datasets associated with aplurality of teams, each team comprising a plurality of jobs and eachjob comprising a KPI dataset; obtaining a predefined service LevelAgreement (SLA) value associated with each team for measuring each teamworkflow; and displaying, on a dashboard, the predefined SLA value andthe KPI datasets associated with the plurality of the teams, whereineach team workflow is visualized as a sequence of bars along a columnrepresenting a time axis in real time.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments of this disclosure are illustrated by way of anexample and not limited in the figures of the accompanying drawings, inwhich like references indicate similar elements and in which:

FIG. 1 is a block diagram illustrating an example computing environmentin which some example embodiments may be implemented;

FIG. 2 is a block diagram illustrating a system architecture in whichsome example embodiments may be implemented;

FIG. 3 is an example screenshot of visualizing a directed diagnosticsKPIs with a SLA for a workflow on a dashboard in accordance with someembodiments;

FIG. 4 is an example screenshot of a consolidated view of a workflow ona dashboard in accordance with some embodiments;

FIG. 5 is an example screenshot of visualizing a directed diagnosticsopportunity SLA workflow on the dashboard in accordance with someembodiments;

FIG. 6 is an example screenshot of visualizing KPIs and an overall SLAworkflow on the dashboard in accordance with some embodiments;

FIG. 7 is an example screenshot of visualizing KPIs on a dashboard withrespect to subdomain names in accordance with some embodiments;

FIG. 8 is another example screenshot of visualizing KPIs and an overallSLA workflow respect to sub-domain names in accordance with someembodiments;

FIG. 9 is another example screenshot of visualizing KPIs and an overallSLA workflow in accordance with some embodiments;

FIG. 10 is an example screenshot of visualizing KPIs and an overall SLAin accordance with some embodiments; and

FIG. 11 is a block diagram illustrating an example computer system inwhich some example embodiments may be implemented.

It is to be understood that both the foregoing general description andthe following detailed description are example and explanatory and areintended to provide further explanations of the invention as claimedonly and are, therefore, not intended to necessarily limit the scope ofthe disclosure.

DETAILED DESCRIPTION

Various example embodiments of the present disclosure will be describedin detail below with reference to the accompanying drawings. Throughoutthe specification, like reference numerals denote like elements havingthe same or similar functions. While specific implementations andexample embodiments are described, it should be understood that this isdone for illustration purposes only. Other components and configurationsmay be used without parting from the spirit and scope of the disclosure,and can be implemented in combinations of the variations provided. Thesevariations shall be described herein as the various embodiments are setforth.

The concepts disclosed herein are directed to systems and methods forvisualizing live status workflows in an entire ecosystem on a singledisplay screen. For example, in a retail sales workflow, there are manyfunctions or operations performed by separate teams or departmentswithin a store, such as inventory status, customer responses, pricecomparisons with local rivals, and sales trends. The data associatedwith these operations may be dynamically updated in real time. Thesystem can bring an amount of information under one screen such that animpact of the changes and updates on the store operations can beassessed and assimilated into the analytics in real time. In someembodiments, the live status of the entire ecosystem can show job statusand workflow details as graphical objects on a graphical user interface(GUI) or a user display screen up to a micro level.

The term “job” as used herein refers to the instructions for a team toperform. For example, in a retail sales environment, a “job” can referto a set of sales processes applied, process parameters, resources used,other data required to determine how a sales service is performed, etc.A job status can be evaluated and manipulated by a job dataset thatdynamically changes in real time. A “workflow” involves in a sequence ofmultiple jobs performed by a team or a department. Example teamworkflows identified in the retail management system may be “Instock”,“Inventory”, “Sales”, etc. A team workflow is represented as acombination of performances of sequential jobs.

In various embodiments, Service Level Agreements (SLAs) are used tomeasure among different jobs performed by teams or internal departmentsin an entire ecosystem of a retailer. A service level agreement (SLA)may be a contract between a service requestor and a service providerthat specifies required levels for a service. The SLAs may becommitments among internal teams or jobs within a retail store. Each SLAmay be associated with datasets or metrics of the jobs assigned for ateam. The goals of the SLAs aim to establish a cooperation and a mutualunderstanding of the teams for maximizing profit and increasingefficiency of the retailer's e-commerce operations. SLA goals are toreduce operational costs, gain new revenue and improve customer service.

The term “Key Performance Indicator (KPI)” as used herein refers to anindicator of a job or process assigned to a team. The KPI is ameasurable parameter that represents how effectively a retail store isachieving key business objectives and demonstrates the effectiveness andperformance of each job of the team under variable operationalconditions.

The KPI is a tool that the retailer can use to regularly measure statusof different workflows and jobs and visualize status and trends of theworkflows associated with their SLAs. Once the goals of the SLAs for aworkflow are defined, the KPIs can be utilized to measure progressestoward those goals and determine how effectively their SLA goals areachieved.

In various embodiments, the system uses a dashboard to monitor KPIsassociated with the SLA goals in real time. The dashboard can show acomplete visualization of the system health so that the related SLAachievement can be recognized and areas to improve can be identifiedpromptly. For example, the dashboard can provide a domain level and anentire level workflow visualization. In case a failure occurs in theworkflow, the dashboard can be used by engineers and decision makers tounderstand visually what exactly a root cause for the failure is. Thevisualized task status displayed on the dashboard may help each decisionmaker identify and define KPIs quickly so as to come up with real-timesolutions.

The system can provide an automatic way for view, manage, analyze,optimize the jobs and workflows in real-time on-site or remotely via theInternet. The users may be developers and decision makers who canmonitor or control different workflows in a retail store.

The system is highly useful for teams in a retail management system. Forexample, the system can evaluate and adjust the jobs performed bydifferent teams efficiently. The system may then reduce a whole cost ofthe operations and management of the retail store. The support teams canuse it to see which jobs need to be optimized and where the issuesreally happen on a live screen, which may then save a lot of time andmanagement cost.

The method is implemented with unique algorithms which are written andexecuted to provide workflow details at micro level and show the livehealth of the entire ecosystem system. The same logic can be scaled upfor other teams under a Global Data and Analytics Platform (GDAP) of theretailer. The same inventive concept can also be used across a pluralityof retails stores in an entire management ecosystem.

FIG. 1 is a block diagram illustrating an example computing environment100 in which some example embodiments may be implemented. The examplecomputing environment 100 generally includes a platform 110, network120, and user device 130.

The platform 110 may be a network-accessible computing platform tocontrol the plurality of tasks and manage the task workflow. Theplatform 110 may be implemented as a cloud-based platform with acomputing infrastructure. The platform 110 may include one or moreservers and databases including processors, memory, applications 113,application program interface (API), and other components that areaccessible via various type of wireless or wired networks. One or moreservers, shown and referred to as a server 112 herein for simplicity,and one or more databases, shown and referred to as a database 111herein for simplicity. These servers may include one or more processorsand memory which may be utilized to process the data associated withworkflows.

The server 112 may be a web server to implement web applications. Theserver 112 communicates with the database 111 to execute one or moresets of processes. The memory in the server 112 may store variousalgorithm generating modules or executed instructions/applications to beexecuted by the processor.

The database 111 may be communicatively coupled to the server 112 toreceive instructions or data from and send data to the server 112 vianetwork 120. The database 111 may store Service Level Agreements (SLAs)values and Key Performance Indicator (KPI) datasets associated with aplurality of teams.

The user device 130 may be a standalone server or a computing device, amobile computing device or a display device located that incommunication with the platform 110 via a network 120 such as a wired orwireless network.

Each job may be associated with a predefined service level agreement(SLA) including different service values and service requirements. Eachjob may be automatically associated with and assigned to a particularteam or department. Different jobs may be dependent and cooperated withto each other. A given job may be dependent upon another job beingcompleted.

FIG. 2 is a block diagram illustrating a cloud-based system architecture200 in which some example embodiments may be implemented. Thecloud-based system architecture 200 can be illustrated as a telemetryarchitecture to be used in an automatic measurement and transmission ofdata from a distant source to the databases in the cloud platform. Thetelemetry architecture can execute data acquisition and transformationactivities across multiple role instances, and store data into multipledatabases. To facilitate data reporting and analytics, job status datacan be aggregated in a centralized database that serves as a main datasource for both pre-defined data and custom reports in the dashboard.

The cloud-based system architecture 200 may include a cloud-basedplatform 270 to communicate with databases and servers for storing,retrieving and analyzing data. In some embodiments, the cloud-basedsystem architecture 200 uses a cloud-based platform, such as an OneOpsplatform. The platform 270 can enable developers to design softwareproducts in a multi-cloud environment.

As illustrated in FIG. 2, the cloud-based system architecture 200 mayuse a database management system (e.g., Oracle database) to gather,store, and analyze the job dataset. The cloud-based system architecture200 may include a database 210 associated with KPI job status and adatabase 220 associated with opportunity engine job status. Thecloud-based system architecture 200 may include various software modulesor executive applications stored in the memory and executed by theprocessors of the platform 270. For example, the cloud-based systemarchitecture 200 include an opportunity engine 240 and a KPI engine 250configured to provide web services. Moreover, the cloud-based systemarchitecture 200 includes a distributed streaming platform (e.g.,Kafka).

The distributed streaming platform includes a Kafka input engine 260 andan Extract Transform Load (ETL) streaming to implement a Kafka outputengine 230. The distributed streaming platform may read, write, andstore streams of data and enable real-time processing of the streams.For example, a stream processor in Kafka can take continual streams ofdata from input topics, perform some processing on this input, andproduce continual streams of data to output topics. The stream processormay take in input streams of sales and an inventory data, and output orexport a stream of reorders and price adjustments based on the salesrecord and the inventory data. The stream processor may effectivelystore and process historical data from the past, and further transformthe data into a format required for analysis.

A plurality of jobs designated for a team may be associated with KeyPerformance Indicator (KPI) datasets which contain information relatedto job and the solution to a particular problem. Variety modules andengines (e.g., algorithms) are designed to analyze the job status andvisually display the workflow in the dashboard 190 in real time.

The dashboard 190 is a data visualization tool or software that allowthe users to understand the analytics of KPI datasets associated with aworkflow of sequential jobs. The data associated with the KPIs can beretrieved from the database or data storages and be visualized on thedashboard 190 as graphical objects (e.g., bar charts) so that the userscan track the health of their business against proposed SLA goals. Forexample, the system can use a central dashboard software (e.g., Tableau)to create bar charts with stored data from databases. As such, the userscan easily and immediately monitor the overall SLA workflow, understandthe KPIs, and improve the business operations through visualrepresentations of the KPIs. The dashboard 190 may perform an analysisof data collected in a particular day and time. The dashboard 190 canautomatically generate reports with the analyzed data as requested bythe users, at anytime and anywhere via the network 120.

In some embodiments, the dashboard 190 can show live status of the KPIsand SLAs workflow in the entire ecosystem. Live status of the jobs andworkflows in the entire ecosystem can be projected and displayed on thedashboard.

In some embodiments, the various screenshots may illustrate thefollowing inventive features of visualizing KPIs and overall SLAworkflow on the dashboard.

-   -   1) Each job is uniquely represented against a dynamic time axis        in the screenshots.    -   2) A length of the bar of each job indicates how much time the        job takes and can be compared with the workflow in previous days        at a glimpse.    -   3) The SLA concept shows the system is within SLA or not.    -   4) Dependency principle indicates the reasons behind the missing        SLAs.    -   5) The dashboard shows the behavior of different jobs at a        detailed level.    -   6) Dynamic images can show the status of jobs at exact live        moments. The dynamic images may be classified as failed image        marks, passed image marks, running image marks, and pending        image marks.    -   7) The dashboard is highly useful for production support teams.

In some embodiments, the live status of the system may include thefollowing key features shown on the dashboard.

-   -   1) Different colors represent different jobs performed by a team        and the length of individual bars represents the time taken by        the jobs.    -   2) The team can easily visualize when the jobs start and end        every day.    -   3) The team can easily visualize which job is consuming a        maximum time in overall workflow every day so that decision        makers can optimize concerned team in the future.    -   4) The SLA is met by an overall workflow.    -   5) Dependency principles can indicate the reasons why a team        misses the SLA.    -   6) The status of jobs is indicated as dynamic images in real        time.

FIG. 3 is a flowchart diagram illustrating an example process ofvisualizing live workflows in accordance with some embodiments. Theprocess 300 may be implemented in the above described systems and mayinclude the following steps. Steps may be omitted or combined dependingon the operations being performed.

In step 302, Key Performance Indicator (KPI) datasets associated with aplurality of teams can be aggregated from a database by a processor.Each team may include a plurality of jobs. Each job is represented as aKPI dataset and displayed on the dashboard.

In step 304, a predefined service Level Agreement (SLA) value associatedwith each team can be obtained for measuring each team workflow. The SLAvalue for each workflow is predefined for the team.

In step 306, the predefined SLA value and the KPI datasets associatedwith the plurality of the teams can be displayed on a dashboard. Eachteam workflow is visualized as a sequence of bars (e.g., team workflowbar) along a column representing a time axis in real time.

FIG. 4 shows a consolidated view of a workflow in the system within acontinuous 15 days. The KPI dataset associated with each job may includea team name, a job identification, a job stating time, a job endingtime, a job duration, a job status, a date, etc. The KPI dataset definesa set of values used to measure the job and team workflow against thepredefined SLA value. These datasets of values are called indicators andare fed to the system in charge of summarizing the job and workflowinformation.

As shown in FIG. 4, multiple jobs are shown on the bottom the screenshotand indicated as different colors. The bar length associated to a jobincludes an amount sequence of periods during each of which a specificjob is performed at a particular time in a day. The length of eachperiod (e.g., job duration) dynamically changes along time axis everyday. A length of an individual bar can represent the time taken by eachjob along a time axis. A team workflow is defined as a combination ofsequential jobs. The team workflow can be visualized as a sequence ofbars along a column representing a time axis in a particular day. Theteam can exactly identify when a job starts and ends in a day. Theindividual bars may not overlapped with each other. A red line crossingthe full dashboard on the screenshot represents the SLA value predefinedfor the team. For example, the SLA value is predefined for the team tobe 6:30 am.

Additionally, each workflow is shown in FIG. 4 by an individual columnin a row axis of continuous 15 days. Displaying a team workflow atdifferent days along a row axis may help to evaluate the team workflowtrend and job efficiency. For example, if the displayed workflowsurpasses or is over the SLA value, the system may consider the teamworkflow is lower than expected. The user may check the reason thatcauses the delay and then optimize the corresponding job assignment. Thescreenshot in FIG. 4 can show a consolidated view of the system with atrend for the continuous 15 days.

In some embodiments, the system provides a mouse-over function for auser to access the KPI dataset of each job and check the job status. Forexample, when a pointer of a computer mouse goes over a job bar, therelated KPI dataset for the job is automatically displayed in a “pop-up”window to show the related job information.

FIG. 5 is an example screenshot of visualizing a directed diagnosticsopportunity SLA workflow on the dashboard with the SLA met on each dayin accordance with some embodiments.

FIG. 6 is an example screenshot of visualizing KPIs and an overall SLAworkflow on the dashboard in accordance with some embodiments. Themultiple jobs are shown on the bottom in the screenshot in FIG. 6 andindicated as different colors. Different team workflows are displayedalong each column during a period of a continuous 3 day. The jobcompleting time is shown on the top of each column of the team workflowbar. FIG. 6 shows all jobs in each team are completed under the SLAvalue. Thus, the SLA value is met by the overall team workflows.

FIG. 7 is another example screenshot of visualizing KPIs and an overallSLA workflow displayed on the dashboard during a period of a continuous3 day.

In the screenshot shown in FIG. 7, multiple team workflows are shown atthe bottom and each team workflow is shown by a different column. A teamcan exactly identify when a job starts and ends every day during aperiod of a continuous 3 day. Each job status can be visualized as aunique predefined mark associated with the team. For example, in theworkflow 701 shown in the extreme right column of “Aug. 21, 2018”, jobsin blue, brown and green each can represent when the job start and whenthey ends. Each job or workflow status may be identified to be one of“Completed”, “Failure”, “Pending”, and “Running”. The status of theworkflow 701 is “Completed”. The corresponding team workflow status isvisualized as a check mark on a top of the sequence of bars associatedwith the team.

In some embodiments, the system may include a plurality of teams, suchas Instock, Inventory, Margin, Sales, Item Trends, etc. Those workflowsprovide visibility of business inventory performance and allow objectivequantitative and qualitative evaluation aligned with the SLA goals.

FIG. 8 is another example screenshot of visualizing KPIs and an overallSLA workflow displayed on a dashboard in accordance with someembodiments.

As shown in FIG. 8, the multiple workflows miss the SLA value of 6:30 amon the two continuous days. In some embodiments, dependency principlescan indicate the reasons why a team misses the SLA. A given job may bedependent upon another job being completed. The decision makers may needto find the reason why the teams do not meet the SLA and figure outwhether a team is dependent on some other teams. A team may comprise atleast one dependent job associated with at least one dependent team.Further, verifying a workflow failure of the team may be based on a KPIdataset of the dependent job in another team. For example, if a teamworkflow is dependent on another team to provide required data tocomplete a job, the decision makers may need to analyze the relatedinformation and find out whether the failed team receives the requireddata from the dependent team on time.

The screenshot in FIG. 8 can show whether the predefined SLA is met bythe overall SLA value or not. A red line crossing the full dashboardrepresents the SLA for the team, SLA is predefined by the team toevaluate the team workflow. As shown in FIG. 8, the predefined SLA forKPI team is at 6:30 am. The teams miss the SLA on July 19 and July 20th.All dependent jobs are shown in red color. The workflows shows that theSLA value is not met because dependent jobs are shown as red color andappear very late, which causes other jobs to delay. As such, thedependent jobs shown as red color with cross marks are indicated as thefailure to show reasons why some team workflows do not meet the SLA.

FIG. 9 is another example screenshot of visualizing KPIs of teamworkflows within a sub-domain in accordance with some embodiments.Multiple team workflows are shown at the bottom and each team workflowis shown by a different column. Multiple workflows in a sub-domain areaare displayed on the dashboard.

Live Status of the System

FIG. 10 is an example screenshot of visualizing workflows for all teamsin an entire management ecosystem in accordance with some embodiments.All KPI datasets processing and analytical results can be sent to asingle dashboard with a real-time visualization tool. The users may usethe dashboard to quickly create new analytical results and understanddeeper into numbers that explain exactly what is going on in the teamworkflow associated with the jobs.

In some embodiments, the dashboard is used to track a wide range of KPIsin an easy-to-understand visual format. For example, the screenshot inFIG. 10 can help the users to identify the failed jobs in the entiremanagement ecosystem immediately.

As shown in FIG. 10, each team workflow status or a job status may beidentified to be one of status, such as “Completed” 1001, “Failure”1002,“Pending” 1003, and “Running” 1004. The “completed” status 1001 of a jobor a team workflow is visualized as a check mark on a top of thesequence of bars associated with the team. The “Failure” status 1002 isdenoted as a cross mark. The “Pending” status 1003 is denoted as a flagmark and means a job has not started yet. “Running” status 1004 isdenoted as a mark or image of a running person and means a job performeris still in a learning stage for preparing for the job. The status markcan be updated as soon as the job is completed. For example, thescreenshot in FIG. 10 can show 2 failed jobs with cross marks and 2running jobs with person image marks on the first column on the leftside and the last column on the right side.

In some embodiments, the team can easily visualize which job isconsuming a maximum time in overall workflow every day so that decisionmakers can optimize concerned team in the future. Based on the KPIdataset, the system may consider some factors when evaluating andoptimizing the tasks in the overall system by identifying the progressof a plurality of teams. For example, the screenshot in FIG. 10 shows ajob 1005 in pink color in the first column on the left side and a job1006 in yellow color on the right side. Both jobs take the maximum timein the system and need to be optimized in the future.

Various embodiments may be implemented to facilitate in optimizing storeoperations in a wide variety of areas.

-   -   1) Opportunity team of directed diagnostics can use the        telemetry to optimize the jobs.    -   2) The information shown in the dashboard can help a team to        meet SLA on a daily basis.    -   3) The user can use the dashboard to visualize the overall        workflow trend.    -   4) Pricing team can use telemetry to check the performance of        on-demand jobs.

The system may facilities decision makers to find any problematic issuesand have the ability to make very fast decisions, and implement changesbased on incoming, real-time data to so that a complete visualization ofthe system health can be achieved.

FIG. 11 illustrates an example computer system 1100 which can be used toperform the systems for inventory monitoring as disclosed herein. Theexample system 1100 can include a processing unit (CPU or processor)1120 and a system bus 1110 that couples various system componentsincluding the system memory 1130 such as read only memory (ROM) 1140 andrandom access memory (RAM) 1150 to the processor 1120. The system 1100can include a cache of high speed memory connected directly with, inclose proximity to, or integrated as part of the processor 1120. Thesystem 1100 copies data from the memory 1130 and/or the storage device1160 to the cache for quick access by the processor 1120. In this way,the cache provides a performance boost that avoids processor 1120 delayswhile waiting for data. These and other modules can control or beconfigured to control the processor 1120 to perform various actions.Other system memory 1130 may be available for use as well. The memory1130 can include multiple different types of memory with differentperformance characteristics. It can be appreciated that the disclosuremay operate on a computing device 1100 with more than one processor 1120or on a group or cluster of computing devices networked together toprovide greater processing capability. The processor 1120 can includeany general purpose processor and a hardware module or software module,such as module 1 1162, module 2 1164, and module 3 1166 stored instorage device 1160, configured to control the processor 1120 as well asa special-purpose processor where software instructions are incorporatedinto the actual processor design. The processor 1120 may essentially bea completely self-contained computing system, containing multiple coresor processors, a bus, memory controller, cache, etc. A multi-coreprocessor may be symmetric or asymmetric.

The system bus 1110 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 1140 or the like, may provide thebasic routine that helps to transfer information between elements withinthe computing device 1100, such as during start-up. The computing device1100 further includes storage devices 1160 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 1160 can include software modules 1162, 1164, 1166 forcontrolling the processor 1120. Other hardware or software modules arecontemplated. The storage device 1160 is connected to the system bus1110 by a drive interface. The drives and the associatedcomputer-readable storage media provide nonvolatile storage ofcomputer-readable instructions, data structures, program modules andother data for the computing device 1100. In one aspect, a hardwaremodule that performs a particular function includes the softwarecomponent stored in a tangible computer-readable storage medium inconnection with the necessary hardware components, such as the processor1120, bus 1110, display 1170, and so forth, to carry out the function.In another aspect, the system can use a processor and computer-readablestorage medium to store instructions which, when executed by theprocessor, cause the processor to perform a method or other specificactions. The basic components and appropriate variations arecontemplated depending on the type of device, such as whether the device1100 is a small, handheld computing device, a desktop computer, or acomputer server.

Although the example embodiment described herein employs the hard disk660, other types of computer-readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 1150, and read only memory (ROM) 1140, may also be used in theexample operating environment. Tangible computer-readable storage media,computer-readable storage devices, or computer-readable memory devices,expressly exclude media such as transitory waves, energy, carriersignals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 1100, an inputdevice 1190 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 1170 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 1100. The communications interface 1180generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Various modifications and changes may be made to theprinciples described herein without following the example embodimentsand applications illustrated and described herein, and without departingfrom the spirit and scope of the disclosure.

What is claimed is:
 1. A computer-implemented method of visualizing liveworkflows, comprising: aggregating, by a processor from a database, KeyPerformance Indicator (KPI) datasets associated with a plurality ofteams, each team comprising a plurality of jobs and each job comprisinga KPI dataset; obtaining a predefined service Level Agreement (SLA)value associated with each team for measuring each team workflow; anddisplaying, on a dashboard, the predefined SLA value and the KPIdatasets associated with the plurality of teams, wherein each teamworkflow is visualized as a sequence of bars along a column representinga time axis in real time.
 2. The method of claim 1, wherein the KPIdataset associated with each job comprises a team name, a jobidentification, a job stating time, a job ending time, a job duration, ajob status, and date.
 3. The method of claim 2, wherein each job isvisualized as a bar the dashboard along the column representing the timeaxis, the bar being displayed with a predefined color and a length ofthe job duration.
 4. The method of claim 1, wherein each team workflowstatus is identified to be one of “Completed”, “Failure”, “Pending”, and“Running”.
 5. The method of claim 1, wherein each team workflow statusis visualized as a unique predefined mark on a top of the sequence ofbars associated with the team.
 6. The method of claim 1, furthercomprising displaying workflows for a team at different days along a rowaxis for evaluating a team workflow trend.
 7. The method of claim 1,wherein a team comprises at least one dependent job associated with atleast one dependent team, and further comprising verifying a workflowfailure of the team based on a KPI dataset of the dependent job.
 8. Themethod of claim 1, further comprising: displaying workflows for allteams in an entire management ecosystem along columns; identifyingfailed jobs on the dashboard; and optimizing the jobs based on the KPIdatasets associated with the jobs.
 9. The method of claim 1, wherein theprocessor and the database are operated on a cloud-based serviceplatform.
 10. A system of visualizing live workflows, the systemcomprising: at least one processor; a computer program productcontaining executable instructions; and a computer-readablenon-transitory storage medium having the executable instructions storedwhich, when executed by the processor, cause the processor to performoperations comprising: aggregating, by the processor from a database,Key Performance Indicator (KPI) datasets associated with a plurality ofteams, each team comprising a plurality of jobs and each job comprisinga KPI dataset; obtaining a predefined service Level Agreement (SLA)value associated with each team for measuring each team workflow; anddisplaying, on a dashboard, the predefined SLA value and the KPIdatasets associated with the plurality of teams, wherein each teamworkflow is visualized as a sequence of bars along a column representinga time axis in real time.
 11. The system of claim 10, wherein the KPIdataset associated with each job comprises a team name, a jobidentification, a job stating time, a job ending time, a job duration, ajob status, and date.
 12. The system of claim 11, wherein each job isvisualized as a bar on the dashboard along the column representing thetime axis, the bar being displayed with a predefined color and a lengthof the job duration.
 13. The system of claim 10, wherein each teamworkflow status is identified to be one of “Completed”, “Failure”,“Pending”, and “Running”.
 14. The system of claim 10, wherein each teamworkflow status is visualized as a unique predefined mark on a top ofthe sequence of bars associated with the team.
 15. The system of claim10, further comprising displaying workflows for a team at different daysalong a row axis for evaluating a team workflow trend.
 16. The system ofclaim 10, wherein a team comprises at least one dependent job associatedwith at least one dependent team, and further comprising verifying aworkflow failure of the team based on a KPI dataset of the dependentjob.
 17. The system of claim 10, further comprising: displayingworkflows for all teams in an entire management ecosystem along columns;identifying failed jobs on the dashboard; and optimizing the jobs basedon the KPI datasets associated with the jobs.
 18. The system of claim10, wherein the processor and the database are operated on a cloud-basedservice platform.
 19. A non-transitory computer-readable storage mediumhaving instructions stored which, when executed by a computing device,cause the computing device to perform operations comprising:aggregating, by a processor from a database, Key Performance Indicator(KPI) datasets associated with a plurality of teams, each teamcomprising a plurality of jobs and each job comprising a KPI dataset;obtaining a predefined service Level Agreement (SLA) value associatedwith each team for measuring each team workflow; and displaying, on adashboard, the predefined SLA value and the KPI datasets associated withthe plurality of teams, wherein each team workflow is visualized as asequence of bars along a column representing a time axis in real time.20. The non-transitory computer-readable storage medium of claim 19,wherein each team workflow status is identified to be one of“Completed”, “Failure”, “Pending”, and “Running”, and each team workflowstatus is visualized as a unique predefined mark on a top of thesequence of bars associated with the team.